{"id":"W4306317452","doi":"10.1145/3511808.3557209","title":"Named Entity-based Question-Answering Pair Generator","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Pipeline (software); Paragraph; Question answering; Generator (circuit theory); Task (project management); Context (archaeology); Abstraction; Simple (philosophy); Text generation; Natural language processing; Argument (complex analysis); Artificial intelligence; Programming language; Engineering; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000739889,0.0001714976,0.0001367509,0.0004071582,0.0003299642,0.0003693423,0.003313747,0.0000311549,0.0001950615],"category_scores_gemma":[0.0001610909,0.0001559552,0.0001057989,0.0003928963,0.00003711287,0.001109205,0.002159023,0.0002349298,0.000120209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003487328,"about_ca_system_score_gemma":0.00007221111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009894462,"about_ca_topic_score_gemma":0.000004747569,"domain_scores_codex":[0.9981387,0.00002276311,0.0005301645,0.0002466681,0.0008627236,0.0001990289],"domain_scores_gemma":[0.9984057,0.0000281239,0.0004330047,0.0004158997,0.000664374,0.00005293223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002182189,0.0001020035,0.0009054601,0.0001221529,0.00005397036,1.085631e-7,0.001408977,0.003413594,0.0002756607,0.9781578,0.00431303,0.01122546],"study_design_scores_gemma":[0.001513783,0.00009195526,0.001690206,0.0002889758,0.00003465945,0.000007230557,0.001176264,0.49568,0.002934957,0.02461394,0.4713458,0.0006222185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1185801,0.00003839226,0.4473714,0.01907678,0.006837462,0.001668346,0.00005290876,0.0006367526,0.4057378],"genre_scores_gemma":[0.9646972,0.00000737848,0.03203985,0.0006347864,0.00007087065,0.000264392,0.00002473443,0.000008049949,0.002252694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9535438,"threshold_uncertainty_score":0.6359668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04509539980294194,"score_gpt":0.2833785768154452,"score_spread":0.2382831770125033,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}